A Logistic Regression Model for Estimating Turbine Mortality at Hydroelectric Generating Stations
Bibliographic record
Abstract
We present a method that allows separation of fish mortality caused by handling and capture techniques from that caused by passage through a turbine. Fish that are naturally entrained into the turbine tube are captured with nets deployed in the turbine tailrace for varying lengths of time. The live or dead status of captured fish is modeled as a binomial response that is a function of the duration of net deployment. Within this model, the intercept is an estimate of the mortality of fish that have spent zero time in the net. For species that do not suffer high mortality from other components of the capture process (such as removal from the net), this intercept may be interpreted as an estimate of turbine mortality. If mortality from other components is high, the intercept cannot be interpreted as turbine mortality without correction for mortality from the other sources. We suggest a modification to the model that allows estimation of mortality from these components. We demonstrate the method with data for 12 species of fish captured at the Annapolis Tidal Generating Station, Nova Scotia, Canada. Acute turbine mortality estimates ranged from 0.0% for sea lamprey Petromyzon marinus to 23.4% for American shad Alosa sapidissima.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".